Modular Meta-Learning with Shrinkage

NIPS 2020, 2020.

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General practitioners who can not afford to collect a large amount of labeled data would be able to take advantage of a pre-trained generic meta-model, and adapt its task-specific components for a new task based on limited data

Abstract:

The modular nature of deep networks allows some components to learn general features, while others learn more task-specific features. When a deep model is then fine-tuned on a new task, each component adapts differently. For example, the input layers of an image classification convnet typically adapt very little, while the output layers m...More

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